Click-through-rate is an important parameter for online advertising, and is one of the more frequently used measures of the success of an online advertising campaign. A CTR provides a measure of ad effectiveness in terms of user response to the ad. One measure of CTR is obtained by dividing the number of users who clicked on an ad on a web page by the number of times the ad was delivered (impressions). For example, if an ad was rendered 1000 times (impressions delivered) and 7 people clicked on it (clicks recorded), then the resulting CTR would be 0.7 percent.
CTR provides a tool for online advertising service providers to use in setting their cost-per-click contract fee structures, as well as a tool for the advertisers to plan their advertising and sales. CTR impacts publisher's revenue in “pay for performance” business model.
The CTR can be computed as the ratio of “clicks to get a full description of the entity” to “views of a reduced version (snippets, listings, thumbnails) of the entity”. This “true” CTR can be calculated after a large number of impressions have been rendered for an advertisement campaign across a representative cross section of the conditions to be encountered throughout the campaign. Prior to commencing the campaign, this true CTR information is not known. Similarly, in the initial stages of the introduction of the entity, the impressions (views) and the clicks are too low to produce a Maximum likelihood estimate (i.e. CTR) using this simple ratio with good confidence.
Estimating CTR before conducting an advertising campaign can help the publisher to set reasonable expectations for the campaign and optimize their plans. This may become problematic if the entity (e.g., a job listing) has a low shelf life.
Richardson et al., “Predicting Clicks: Estimating the Click-Through Rate for New Ads,” International World Wide Web Conference Committee, May 8-12, 2007, Banff, Alberta, Canada, states that it is most beneficial for the search engine to place best performing ads first, and notes that, because the probability of clicking on an ad drops so significantly with ad position, the accuracy with which its CTR is estimated can have a significant effect on revenues.
Richardson et al. identify five features of a new advertisement that can be used to predict the CTR of the ad. Richardson et al. use a logistic regression model to predict CTR based on the following factors.
(1) Appearance: Is the ad aesthetically pleasing?
(2) Attention Capture: Does the ad draw the user in?
(3) Reputation: Is the advertiser a known or reputable brand?
(4) Landing page quality.
(5) Relevance: How relevant is the ad to search query term?
Improved methods of CTR prediction are desired.